Wine Quality Prediction using Machine Learning and Hybrid Modeling

Gawale, Avinash Sanjay (2022) Wine Quality Prediction using Machine Learning and Hybrid Modeling. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (2MB) | Preview
Preview	PDF (Configuration manual) Download (3MB) | Preview

Abstract

Globally, there has been an upsurge in wine consumption. 31 million metric tons of wine are delivered globally, which is a significant quantity. Considering how extremely competitive the wine market is, the wine industry is investing in innovative technologies for both wine-producing and selling processes. Technology has made it possible for businesses to provide consumers with high-quality wine by introducing machine learning and hybrid modeling techniques for wine quality prediction. The study is being carried out to implement the Decision Tree (DT), Random Forest (RF) , and Extreme Gradient Boosting (XGBoost) in wine quality prediction and to identify machine learning techniques’ role as hybrid models in wine quality prediction. The dataset for wine quality is available publicly on the repository of UCI machine learning and dataset from said database has been used in the study. Data interpretation has been performed based on accuracy, precision, recall and f1 Score. A comparison of developed models carried out. The models tested include Decision Tree Classifier, Random Forest Classifier, XGboost Classifier and a Hybrid Model. The results indicate that the most accurate and precise model is that of Random Forest with the highest accuracy, precision, recall and f1 score.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Wine Prediction; Decision Tree; Random Forest; XGBoost; Hybrid Model
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > S Agriculture (General) Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	24 Jan 2023 16:13
Last Modified:	03 Mar 2023 12:10
URI:	https://norma.ncirl.ie/id/eprint/6124

Actions (login required)

View Item